{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import QUANTAXIS as QA\n",
    "try:\n",
    "    assert QA.__version__>='1.1.0'\n",
    "except AssertionError:\n",
    "    print('pip install QUANTAXIS >= 1.1.0 请升级QUANTAXIS后再运行此示例')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "首先确定你已经完成了对于QUANTAXIS的基础认知,以及在本地存储完毕了QUANTAXIS的数据库\n"
     ]
    }
   ],
   "source": [
    "print('首先确定你已经完成了对于QUANTAXIS的基础认知,以及在本地存储完毕了QUANTAXIS的数据库')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QUANTAXIS 回测的一些基础知识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  QA回测的核心是两个类\n",
    "\n",
    "```\n",
    "QA_BacktestBroker\n",
    "QA_Account\n",
    "```\n",
    "\n",
    "##  回测数据的引入/迭代\n",
    "\n",
    "```\n",
    "QA.QA_fetch_stock_day_adv\n",
    "QA.QA_fetch_stock_min_adv\n",
    "```\n",
    "\n",
    "##  指标的计算\n",
    "\n",
    "```\n",
    "DataStruct.add_func\n",
    "```\n",
    "\n",
    "##  对于账户的灵活运用\n",
    "\n",
    "```\n",
    "QA_Account\n",
    "QA_Risk\n",
    "QA_Portfolio\n",
    "QA_PortfolioView\n",
    "QA_User\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP1 初始化账户,初始化回测broker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Account=QA.QA_Account()\n",
    "Broker=QA.QA_BacktestBroker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 打印账户的信息\n",
    "# try:\n",
    "#     from pprint import  pprint as print\n",
    "# except:\n",
    "#     pass\n",
    "# print(Account.message)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 首先讲解Account类:\n",
    "\n",
    "QA_Account在初始化的时候,可以自己指定很多信息:\n",
    "\n",
    "```\n",
    "\n",
    "    QA_Account(\n",
    "        strategy_name=None, user_cookie=None, portfolio_cookie=None, account_cookie=None,\n",
    "        market_type=MARKET_TYPE.STOCK_CN, frequence=FREQUENCE.DAY, broker=BROKER_TYPE.BACKETEST,\n",
    "        init_hold={}, init_cash=1000000, commission_coeff=0.00025, tax_coeff=0.0015,\n",
    "        margin_level=False, allow_t0=False, allow_sellopen=False,\n",
    "        running_environment=RUNNING_ENVIRONMENT.BACKETEST)\n",
    "\n",
    "        :param [str] strategy_name:  策略名称\n",
    "        :param [str] user_cookie:   用户cookie\n",
    "        :param [str] portfolio_cookie: 组合cookie\n",
    "        :param [str] account_cookie:   账户cookie\n",
    "\n",
    "        :param [dict] init_hold         初始化时的股票资产\n",
    "        :param [float] init_cash:         初始化资金\n",
    "        :param [float] commission_coeff:  交易佣金 :默认 万2.5   float 类型\n",
    "        :param [float] tax_coeff:         印花税   :默认 千1.5   float 类型\n",
    "\n",
    "        :param [Bool] margin_level:      保证金比例 默认False\n",
    "        :param [Bool] allow_t0:          是否允许t+0交易  默认False\n",
    "        :param [Bool] allow_sellopen:    是否允许卖空开仓  默认False\n",
    "\n",
    "        :param [QA.PARAM] market_type:   市场类别 默认QA.MARKET_TYPE.STOCK_CN A股股票\n",
    "        :param [QA.PARAM] frequence:     账户级别 默认日线QA.FREQUENCE.DAY\n",
    "        :param [QA.PARAM] broker:        BROEKR类 默认回测 QA.BROKER_TYPE.BACKTEST\n",
    "        :param [QA.PARAM] running_environment 当前运行环境 默认Backtest\n",
    "\n",
    "        # 2018/06/11 init_assets 从float变为dict,并且不作为输入,作为只读属性\n",
    "        #  :param [float] init_assets:       初始资产  默认 1000000 元 （100万）\n",
    "        init_assets:{\n",
    "            cash: xxx,\n",
    "            stock: {'000001':2000},\n",
    "            init_date: '2018-02-05',\n",
    "            init_datetime: '2018-02-05 15:00:00'\n",
    "        }\n",
    "        # 2018/06/11 取消在初始化的时候的cash和history输入\n",
    "        # :param [list] cash:              可用现金  默认 是 初始资产  list 类型\n",
    "        # :param [list] history:           交易历史\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重设账户初始资金\n",
    "\n",
    "Account.reset_assets(200000)\n",
    "Account.account_cookie='JCSC_EXAMPLE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cash': 200000, 'hold': {}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.init_assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Account 有很多方法,暂时不详细展开,我们先直接进入下一步"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SETP2:引入回测的市场数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "引入方法非常简单,直接使用QA_fetch_stock_day_adv系列即可\n",
    "\n",
    "- code 可以是多种多样的选取方式\n",
    "\n",
    "```python\n",
    "1. QA.QA_fetch_stock_list_adv().code.tolist() # 获取全市场的股票代码\n",
    "2. QA.QA_fetch_stock_block_adv().get_block('云计算').code  # 按版块选取\n",
    "3. code= ['000001','000002'] # 自己指定\n",
    "```\n",
    "- 数据获取后,to_qfq() 即可获得前复权数据\n",
    "\n",
    "```python\n",
    "data=DataSturct.to_qfq()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QA.QA_fetch_stock_list_adv().code.tolist()\n",
    "# QA.QA_fetch_stock_block_adv().get_block('云计算').code\n",
    "#codelist=QA.QA_fetch_stock_block_adv().get_block('云计算').code\n",
    "codelist=['000001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=QA.QA_fetch_stock_day_adv(codelist,'2017-09-01','2018-05-20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "< QA_DataStruct_Stock_day with 1 securities >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.to_qfq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP3:计算一些指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "指标的计算可以在回测前,也可以在回测中进行\n",
    "\n",
    "回测前的计算则是批量计算,效率较高\n",
    "\n",
    "回测中的计算,效率略低,但代码量较小,易于理解\n",
    "\n",
    "PS: 指标的相关介绍参见 [QUANTAXIS的指标系统](https://github.com/QUANTAXIS/QUANTAXIS/blob/master/Documents/indicators.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "def MACD_JCSC(dataframe,SHORT=12,LONG=26,M=9):\n",
    "    \"\"\"\n",
    "    1.DIF向上突破DEA，买入信号参考。\n",
    "    \n",
    "    2.DIF向下跌破DEA，卖出信号参考。\n",
    "    \"\"\"\n",
    "    CLOSE=dataframe.close\n",
    "    DIFF =QA.EMA(CLOSE,SHORT) - QA.EMA(CLOSE,LONG)\n",
    "    DEA = QA.EMA(DIFF,M)\n",
    "    MACD =2*(DIFF-DEA)\n",
    "\n",
    "    CROSS_JC=QA.CROSS(DIFF,DEA)\n",
    "    CROSS_SC=QA.CROSS(DEA,DIFF)\n",
    "    ZERO=0\n",
    "    return pd.DataFrame({'DIFF':DIFF,'DEA':DEA,'MACD':MACD,'CROSS_JC':CROSS_JC,'CROSS_SC':CROSS_SC,'ZERO':ZERO})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind=data.add_func(MACD_JCSC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb23cb9f98>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind.xs(codelist[0],level=1)['2018-01'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DIFF</th>\n",
       "      <th>DEA</th>\n",
       "      <th>MACD</th>\n",
       "      <th>CROSS_JC</th>\n",
       "      <th>CROSS_SC</th>\n",
       "      <th>ZERO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.110686</td>\n",
       "      <td>0.103225</td>\n",
       "      <td>0.014921</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.100387</td>\n",
       "      <td>0.102657</td>\n",
       "      <td>-0.004541</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.084801</td>\n",
       "      <td>0.099086</td>\n",
       "      <td>-0.028570</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.075607</td>\n",
       "      <td>0.094390</td>\n",
       "      <td>-0.037566</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.040453</td>\n",
       "      <td>0.083603</td>\n",
       "      <td>-0.086299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>0.022012</td>\n",
       "      <td>0.071285</td>\n",
       "      <td>-0.098545</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>0.038391</td>\n",
       "      <td>0.064706</td>\n",
       "      <td>-0.052629</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>0.045208</td>\n",
       "      <td>0.060806</td>\n",
       "      <td>-0.031197</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>0.061988</td>\n",
       "      <td>0.061043</td>\n",
       "      <td>0.001890</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-15</th>\n",
       "      <td>0.126236</td>\n",
       "      <td>0.074081</td>\n",
       "      <td>0.104309</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>0.175134</td>\n",
       "      <td>0.094292</td>\n",
       "      <td>0.161683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>0.213839</td>\n",
       "      <td>0.118201</td>\n",
       "      <td>0.191275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>0.280788</td>\n",
       "      <td>0.150718</td>\n",
       "      <td>0.260139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>0.336418</td>\n",
       "      <td>0.187858</td>\n",
       "      <td>0.297119</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>0.347467</td>\n",
       "      <td>0.219780</td>\n",
       "      <td>0.255374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>0.368907</td>\n",
       "      <td>0.249605</td>\n",
       "      <td>0.238603</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>0.380702</td>\n",
       "      <td>0.275825</td>\n",
       "      <td>0.209755</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>0.350521</td>\n",
       "      <td>0.290764</td>\n",
       "      <td>0.119515</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>0.310920</td>\n",
       "      <td>0.294795</td>\n",
       "      <td>0.032249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>0.251630</td>\n",
       "      <td>0.286162</td>\n",
       "      <td>-0.069064</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>0.195134</td>\n",
       "      <td>0.267957</td>\n",
       "      <td>-0.145646</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>0.180544</td>\n",
       "      <td>0.250474</td>\n",
       "      <td>-0.139859</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                DIFF       DEA      MACD  CROSS_JC  CROSS_SC  ZERO\n",
       "date                                                              \n",
       "2018-01-02  0.110686  0.103225  0.014921         1         0     0\n",
       "2018-01-03  0.100387  0.102657 -0.004541         0         1     0\n",
       "2018-01-04  0.084801  0.099086 -0.028570         0         0     0\n",
       "2018-01-05  0.075607  0.094390 -0.037566         0         0     0\n",
       "2018-01-08  0.040453  0.083603 -0.086299         0         0     0\n",
       "2018-01-09  0.022012  0.071285 -0.098545         0         0     0\n",
       "2018-01-10  0.038391  0.064706 -0.052629         0         0     0\n",
       "2018-01-11  0.045208  0.060806 -0.031197         0         0     0\n",
       "2018-01-12  0.061988  0.061043  0.001890         1         0     0\n",
       "2018-01-15  0.126236  0.074081  0.104309         0         0     0\n",
       "2018-01-16  0.175134  0.094292  0.161683         0         0     0\n",
       "2018-01-17  0.213839  0.118201  0.191275         0         0     0\n",
       "2018-01-18  0.280788  0.150718  0.260139         0         0     0\n",
       "2018-01-19  0.336418  0.187858  0.297119         0         0     0\n",
       "2018-01-22  0.347467  0.219780  0.255374         0         0     0\n",
       "2018-01-23  0.368907  0.249605  0.238603         0         0     0\n",
       "2018-01-24  0.380702  0.275825  0.209755         0         0     0\n",
       "2018-01-25  0.350521  0.290764  0.119515         0         0     0\n",
       "2018-01-26  0.310920  0.294795  0.032249         0         0     0\n",
       "2018-01-29  0.251630  0.286162 -0.069064         0         1     0\n",
       "2018-01-30  0.195134  0.267957 -0.145646         0         0     0\n",
       "2018-01-31  0.180544  0.250474 -0.139859         0         0     0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind.xs(codelist[0],level=1)['2018-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.031197</td>\n",
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       "      <th>2018-01-12</th>\n",
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       "      <td>0.104309</td>\n",
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       "      <td>0.191275</td>\n",
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       "      <td>0.294795</td>\n",
       "      <td>0.032249</td>\n",
       "      <td>0</td>\n",
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       "      <th>2018-01-29</th>\n",
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      ],
      "text/plain": [
       "                       DIFF       DEA      MACD  CROSS_JC  CROSS_SC  ZERO\n",
       "date       code                                                          \n",
       "2018-01-02 000001  0.110686  0.103225  0.014921         1         0     0\n",
       "2018-01-03 000001  0.100387  0.102657 -0.004541         0         1     0\n",
       "2018-01-04 000001  0.084801  0.099086 -0.028570         0         0     0\n",
       "2018-01-05 000001  0.075607  0.094390 -0.037566         0         0     0\n",
       "2018-01-08 000001  0.040453  0.083603 -0.086299         0         0     0\n",
       "2018-01-09 000001  0.022012  0.071285 -0.098545         0         0     0\n",
       "2018-01-10 000001  0.038391  0.064706 -0.052629         0         0     0\n",
       "2018-01-11 000001  0.045208  0.060806 -0.031197         0         0     0\n",
       "2018-01-12 000001  0.061988  0.061043  0.001890         1         0     0\n",
       "2018-01-15 000001  0.126236  0.074081  0.104309         0         0     0\n",
       "2018-01-16 000001  0.175134  0.094292  0.161683         0         0     0\n",
       "2018-01-17 000001  0.213839  0.118201  0.191275         0         0     0\n",
       "2018-01-18 000001  0.280788  0.150718  0.260139         0         0     0\n",
       "2018-01-19 000001  0.336418  0.187858  0.297119         0         0     0\n",
       "2018-01-22 000001  0.347467  0.219780  0.255374         0         0     0\n",
       "2018-01-23 000001  0.368907  0.249605  0.238603         0         0     0\n",
       "2018-01-24 000001  0.380702  0.275825  0.209755         0         0     0\n",
       "2018-01-25 000001  0.350521  0.290764  0.119515         0         0     0\n",
       "2018-01-26 000001  0.310920  0.294795  0.032249         0         0     0\n",
       "2018-01-29 000001  0.251630  0.286162 -0.069064         0         1     0\n",
       "2018-01-30 000001  0.195134  0.267957 -0.145646         0         0     0\n",
       "2018-01-31 000001  0.180544  0.250474 -0.139859         0         0     0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind.loc['2018-01',slice(None)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SETP4:选取回测的开始和结束日期,构建回测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "receive deal\n",
      "receive deal\n",
      "receive deal\n",
      "receive deal\n",
      "receive deal\n",
      "receive deal\n",
      "receive deal\n"
     ]
    }
   ],
   "source": [
    "data_forbacktest=data.select_time('2018-01-01','2018-05-01')\n",
    "\n",
    "\n",
    "for items in data_forbacktest.panel_gen:\n",
    "    for item in items.security_gen:\n",
    "        daily_ind=ind.loc[item.index]\n",
    "        if daily_ind.CROSS_JC.iloc[0]>0:\n",
    "            order=Account.send_order(\n",
    "                code=item.code[0], \n",
    "                time=item.date[0], \n",
    "                amount=1000, \n",
    "                towards=QA.ORDER_DIRECTION.BUY, \n",
    "                price=0, \n",
    "                order_model=QA.ORDER_MODEL.CLOSE, \n",
    "                amount_model=QA.AMOUNT_MODEL.BY_AMOUNT\n",
    "                )\n",
    "            #print(item.to_json()[0])\n",
    "            Broker.receive_order(QA.QA_Event(order=order,market_data=item))\n",
    "            \n",
    "            \n",
    "            trade_mes=Broker.query_orders(Account.account_cookie,'filled')\n",
    "            res=trade_mes.loc[order.account_cookie,order.realorder_id]\n",
    "            order.trade(res.trade_id,res.trade_price,res.trade_amount,res.trade_time)\n",
    "        elif daily_ind.CROSS_SC.iloc[0]>0:\n",
    "            if Account.sell_available.get(item.code[0], 0)>0:\n",
    "                order=Account.send_order(\n",
    "                    code=item.code[0], \n",
    "                    time=item.date[0], \n",
    "                    amount=Account.sell_available.get(item.code[0], 0), \n",
    "                    towards=QA.ORDER_DIRECTION.SELL, \n",
    "                    price=0, \n",
    "                    order_model=QA.ORDER_MODEL.MARKET, \n",
    "                    amount_model=QA.AMOUNT_MODEL.BY_AMOUNT\n",
    "                    )\n",
    "                Broker.receive_order(QA.QA_Event(order=order,market_data=item))\n",
    "\n",
    "\n",
    "                trade_mes=Broker.query_orders(Account.account_cookie,'filled')\n",
    "                res=trade_mes.loc[order.account_cookie,order.realorder_id]\n",
    "                order.trade(res.trade_id,res.trade_price,res.trade_amount,res.trade_time)\n",
    "    Account.settle()\n",
    "            \n",
    "        #break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP5: 分析账户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['2018-01-02 00:00:00',\n",
       "  '000001',\n",
       "  13.7,\n",
       "  1000,\n",
       "  186295.0,\n",
       "  'Order_FemEzpZ0',\n",
       "  'Order_FemEzpZ0',\n",
       "  'Trade_TQzDbd0g',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5,\n",
       "  0,\n",
       "  None],\n",
       " ['2018-01-03 00:00:00',\n",
       "  '000001',\n",
       "  13.53,\n",
       "  -1000,\n",
       "  199806.47,\n",
       "  'Order_mnKfLY0M',\n",
       "  'Order_mnKfLY0M',\n",
       "  'Trade_ldhSrMYa',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5,\n",
       "  13.530000000000001,\n",
       "  None],\n",
       " ['2018-01-12 00:00:00',\n",
       "  '000001',\n",
       "  13.55,\n",
       "  1000,\n",
       "  186251.47,\n",
       "  'Order_nWrOyhV4',\n",
       "  'Order_nWrOyhV4',\n",
       "  'Trade_AkSzOM7i',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5,\n",
       "  0,\n",
       "  None],\n",
       " ['2018-01-29 00:00:00',\n",
       "  '000001',\n",
       "  13.93,\n",
       "  -1000,\n",
       "  200162.54,\n",
       "  'Order_ygQNXbEJ',\n",
       "  'Order_ygQNXbEJ',\n",
       "  'Trade_HW4C7STK',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5,\n",
       "  13.93,\n",
       "  None],\n",
       " ['2018-03-08 00:00:00',\n",
       "  '000001',\n",
       "  12.11,\n",
       "  1000,\n",
       "  188047.54,\n",
       "  'Order_osmdlg2w',\n",
       "  'Order_osmdlg2w',\n",
       "  'Trade_UpRMLDi9',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5,\n",
       "  0,\n",
       "  None],\n",
       " ['2018-03-26 00:00:00',\n",
       "  '000001',\n",
       "  11.03,\n",
       "  -1000,\n",
       "  199061.51,\n",
       "  'Order_07LT6nZE',\n",
       "  'Order_07LT6nZE',\n",
       "  'Trade_kQDMnIJH',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5,\n",
       "  11.03,\n",
       "  None],\n",
       " ['2018-04-10 00:00:00',\n",
       "  '000001',\n",
       "  11.42,\n",
       "  1000,\n",
       "  187636.51,\n",
       "  'Order_r9Mdl61s',\n",
       "  'Order_r9Mdl61s',\n",
       "  'Trade_zA7EdDj0',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5,\n",
       "  0,\n",
       "  None]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>code</th>\n",
       "      <th>price</th>\n",
       "      <th>amount</th>\n",
       "      <th>cash</th>\n",
       "      <th>order_id</th>\n",
       "      <th>realorder_id</th>\n",
       "      <th>trade_id</th>\n",
       "      <th>account_cookie</th>\n",
       "      <th>commission</th>\n",
       "      <th>tax</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>13.70</td>\n",
       "      <td>1000</td>\n",
       "      <td>186295.00</td>\n",
       "      <td>Order_FemEzpZ0</td>\n",
       "      <td>Order_FemEzpZ0</td>\n",
       "      <td>Trade_TQzDbd0g</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>13.53</td>\n",
       "      <td>-1000</td>\n",
       "      <td>199806.47</td>\n",
       "      <td>Order_mnKfLY0M</td>\n",
       "      <td>Order_mnKfLY0M</td>\n",
       "      <td>Trade_ldhSrMYa</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>13.53</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-12 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>13.55</td>\n",
       "      <td>1000</td>\n",
       "      <td>186251.47</td>\n",
       "      <td>Order_nWrOyhV4</td>\n",
       "      <td>Order_nWrOyhV4</td>\n",
       "      <td>Trade_AkSzOM7i</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-29 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>13.93</td>\n",
       "      <td>-1000</td>\n",
       "      <td>200162.54</td>\n",
       "      <td>Order_ygQNXbEJ</td>\n",
       "      <td>Order_ygQNXbEJ</td>\n",
       "      <td>Trade_HW4C7STK</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>13.93</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-08 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>12.11</td>\n",
       "      <td>1000</td>\n",
       "      <td>188047.54</td>\n",
       "      <td>Order_osmdlg2w</td>\n",
       "      <td>Order_osmdlg2w</td>\n",
       "      <td>Trade_UpRMLDi9</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-03-26 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>11.03</td>\n",
       "      <td>-1000</td>\n",
       "      <td>199061.51</td>\n",
       "      <td>Order_07LT6nZE</td>\n",
       "      <td>Order_07LT6nZE</td>\n",
       "      <td>Trade_kQDMnIJH</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>11.03</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-04-10 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>11.42</td>\n",
       "      <td>1000</td>\n",
       "      <td>187636.51</td>\n",
       "      <td>Order_r9Mdl61s</td>\n",
       "      <td>Order_r9Mdl61s</td>\n",
       "      <td>Trade_zA7EdDj0</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime    code  price  amount       cash        order_id  \\\n",
       "0  2018-01-02 00:00:00  000001  13.70    1000  186295.00  Order_FemEzpZ0   \n",
       "1  2018-01-03 00:00:00  000001  13.53   -1000  199806.47  Order_mnKfLY0M   \n",
       "2  2018-01-12 00:00:00  000001  13.55    1000  186251.47  Order_nWrOyhV4   \n",
       "3  2018-01-29 00:00:00  000001  13.93   -1000  200162.54  Order_ygQNXbEJ   \n",
       "4  2018-03-08 00:00:00  000001  12.11    1000  188047.54  Order_osmdlg2w   \n",
       "5  2018-03-26 00:00:00  000001  11.03   -1000  199061.51  Order_07LT6nZE   \n",
       "6  2018-04-10 00:00:00  000001  11.42    1000  187636.51  Order_r9Mdl61s   \n",
       "\n",
       "     realorder_id        trade_id account_cookie  commission    tax message  \n",
       "0  Order_FemEzpZ0  Trade_TQzDbd0g   JCSC_EXAMPLE           5   0.00    None  \n",
       "1  Order_mnKfLY0M  Trade_ldhSrMYa   JCSC_EXAMPLE           5  13.53    None  \n",
       "2  Order_nWrOyhV4  Trade_AkSzOM7i   JCSC_EXAMPLE           5   0.00    None  \n",
       "3  Order_ygQNXbEJ  Trade_HW4C7STK   JCSC_EXAMPLE           5  13.93    None  \n",
       "4  Order_osmdlg2w  Trade_UpRMLDi9   JCSC_EXAMPLE           5   0.00    None  \n",
       "5  Order_07LT6nZE  Trade_kQDMnIJH   JCSC_EXAMPLE           5  11.03    None  \n",
       "6  Order_r9Mdl61s  Trade_zA7EdDj0   JCSC_EXAMPLE           5   0.00    None  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.history_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.concat([Account.daily_hold.reset_index().set_index('date'),pd.Series(data=None,index=pd.to_datetime(Account.trade_range).set_names('date'),name='predrop')],axis=1).ffill().drop(['predrop'],axis=1).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb242d8c88>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Account.daily_cash.cash.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>000001</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>account_cookie</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-15</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-08</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-09</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-26</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-27</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
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       "    <tr>\n",
       "      <th>2018-03-02</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-03-05</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-03-06</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-07</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-09</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-12</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-13</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-14</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-15</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-16</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-19</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-28</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-30</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-03</th>\n",
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       "    <tr>\n",
       "      <th>2018-04-04</th>\n",
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       "    <tr>\n",
       "      <th>2018-04-09</th>\n",
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       "    <tr>\n",
       "      <th>2018-04-10</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           000001\n",
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       "...                           ...\n",
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       "\n",
       "[64 rows x 1 columns]"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.daily_hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Risk=QA.QA_Risk(Account)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       " 'timeindex': ['2018-01-02',\n",
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       "  '2018-01-29',\n",
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       " 'totaltimeindex': ['2018-01-02',\n",
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       "  '2018-03-16',\n",
       "  '2018-03-19',\n",
       "  '2018-03-20',\n",
       "  '2018-03-21',\n",
       "  '2018-03-22',\n",
       "  '2018-03-23',\n",
       "  '2018-03-26',\n",
       "  '2018-03-27',\n",
       "  '2018-03-28',\n",
       "  '2018-03-29',\n",
       "  '2018-03-30',\n",
       "  '2018-04-02',\n",
       "  '2018-04-03',\n",
       "  '2018-04-04',\n",
       "  '2018-04-09',\n",
       "  '2018-04-10'],\n",
       " 'ir': -2.0}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "code\n",
       "000001    11420.0\n",
       "Name: (2018-04-10 00:00:00, JCSC_EXAMPLE), dtype: float64"
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "Risk.market_value.diff().iloc[-1]"
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  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                                      cash             datetime       date  \\\n",
       "datetime            account_cookie                                           \n",
       "2018-01-02 00:00:00 JCSC_EXAMPLE    186295  2018-01-02 00:00:00 2018-01-02   \n",
       "2018-01-03 00:00:00 JCSC_EXAMPLE    199806  2018-01-03 00:00:00 2018-01-03   \n",
       "2018-01-12 00:00:00 JCSC_EXAMPLE    186251  2018-01-12 00:00:00 2018-01-12   \n",
       "2018-01-29 00:00:00 JCSC_EXAMPLE    200163  2018-01-29 00:00:00 2018-01-29   \n",
       "2018-03-08 00:00:00 JCSC_EXAMPLE    188048  2018-03-08 00:00:00 2018-03-08   \n",
       "2018-03-26 00:00:00 JCSC_EXAMPLE    199062  2018-03-26 00:00:00 2018-03-26   \n",
       "2018-04-10 00:00:00 JCSC_EXAMPLE    187637  2018-04-10 00:00:00 2018-04-10   \n",
       "\n",
       "                                   account_cookie  \n",
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       "2018-01-02 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-03 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-12 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-29 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-03-08 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-03-26 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-10 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.account.cash_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=Account.cash_table.drop_duplicates(subset='date', keep='last').set_index(['date', 'account_cookie'], drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "res=Account.cash_table.drop_duplicates(subset='date', keep='last')\n",
    "\n",
    "zx=pd.concat([res.set_index('date'),pd.Series(data=None,index=pd.to_datetime(Account.trade_range),name='predrop')],axis=1).ffill().drop(['predrop'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "zxx=pd.to_datetime(Account.trade_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05',\n",
       "               '2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11',\n",
       "               '2018-01-12', '2018-01-15', '2018-01-16', '2018-01-17',\n",
       "               '2018-01-18', '2018-01-19', '2018-01-22', '2018-01-23',\n",
       "               '2018-01-24', '2018-01-25', '2018-01-26', '2018-01-29',\n",
       "               '2018-01-30', '2018-01-31', '2018-02-01', '2018-02-02',\n",
       "               '2018-02-05', '2018-02-06', '2018-02-07', '2018-02-08',\n",
       "               '2018-02-09', '2018-02-12', '2018-02-13', '2018-02-14',\n",
       "               '2018-02-22', '2018-02-23', '2018-02-26', '2018-02-27',\n",
       "               '2018-02-28', '2018-03-01', '2018-03-02', '2018-03-05',\n",
       "               '2018-03-06', '2018-03-07', '2018-03-08', '2018-03-09',\n",
       "               '2018-03-12', '2018-03-13', '2018-03-14', '2018-03-15',\n",
       "               '2018-03-16', '2018-03-19', '2018-03-20', '2018-03-21',\n",
       "               '2018-03-22', '2018-03-23', '2018-03-26', '2018-03-27',\n",
       "               '2018-03-28', '2018-03-29', '2018-03-30', '2018-04-02',\n",
       "               '2018-04-03', '2018-04-04', '2018-04-09', '2018-04-10'],\n",
       "              dtype='datetime64[ns]', name='date', freq=None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zxx.set_names('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05',\n",
       "               '2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11',\n",
       "               '2018-01-12', '2018-01-15', '2018-01-16', '2018-01-17',\n",
       "               '2018-01-18', '2018-01-19', '2018-01-22', '2018-01-23',\n",
       "               '2018-01-24', '2018-01-25', '2018-01-26', '2018-01-29',\n",
       "               '2018-01-30', '2018-01-31', '2018-02-01', '2018-02-02',\n",
       "               '2018-02-05', '2018-02-06', '2018-02-07', '2018-02-08',\n",
       "               '2018-02-09', '2018-02-12', '2018-02-13', '2018-02-14',\n",
       "               '2018-02-22', '2018-02-23', '2018-02-26', '2018-02-27',\n",
       "               '2018-02-28', '2018-03-01', '2018-03-02', '2018-03-05',\n",
       "               '2018-03-06', '2018-03-07', '2018-03-08', '2018-03-09',\n",
       "               '2018-03-12', '2018-03-13', '2018-03-14', '2018-03-15',\n",
       "               '2018-03-16', '2018-03-19', '2018-03-20', '2018-03-21',\n",
       "               '2018-03-22', '2018-03-23', '2018-03-26', '2018-03-27',\n",
       "               '2018-03-28', '2018-03-29', '2018-03-30', '2018-04-02',\n",
       "               '2018-04-03', '2018-04-04', '2018-04-09', '2018-04-10'],\n",
       "              dtype='datetime64[ns]', name='date', freq=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.daily_hold.index.levels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.Series(data=None,index=Account.trade_range,name='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        account_cookie\n",
       "2018-01-02  JCSC_EXAMPLE      13700.0\n",
       "2018-01-03  JCSC_EXAMPLE          0.0\n",
       "2018-01-04  JCSC_EXAMPLE          0.0\n",
       "2018-01-05  JCSC_EXAMPLE          0.0\n",
       "2018-01-08  JCSC_EXAMPLE          0.0\n",
       "2018-01-09  JCSC_EXAMPLE          0.0\n",
       "2018-01-10  JCSC_EXAMPLE          0.0\n",
       "2018-01-11  JCSC_EXAMPLE          0.0\n",
       "2018-01-12  JCSC_EXAMPLE      13550.0\n",
       "2018-01-15  JCSC_EXAMPLE      14200.0\n",
       "2018-01-16  JCSC_EXAMPLE      14200.0\n",
       "2018-01-17  JCSC_EXAMPLE      14230.0\n",
       "2018-01-18  JCSC_EXAMPLE      14720.0\n",
       "2018-01-19  JCSC_EXAMPLE      14800.0\n",
       "2018-01-22  JCSC_EXAMPLE      14440.0\n",
       "2018-01-23  JCSC_EXAMPLE      14650.0\n",
       "2018-01-24  JCSC_EXAMPLE      14640.0\n",
       "2018-01-25  JCSC_EXAMPLE      14200.0\n",
       "2018-01-26  JCSC_EXAMPLE      14050.0\n",
       "2018-01-29  JCSC_EXAMPLE          0.0\n",
       "2018-01-30  JCSC_EXAMPLE          0.0\n",
       "2018-01-31  JCSC_EXAMPLE          0.0\n",
       "2018-02-01  JCSC_EXAMPLE          0.0\n",
       "2018-02-02  JCSC_EXAMPLE          0.0\n",
       "2018-02-05  JCSC_EXAMPLE          0.0\n",
       "2018-02-06  JCSC_EXAMPLE          0.0\n",
       "2018-02-07  JCSC_EXAMPLE          0.0\n",
       "2018-02-08  JCSC_EXAMPLE          0.0\n",
       "2018-02-09  JCSC_EXAMPLE          0.0\n",
       "2018-02-12  JCSC_EXAMPLE          0.0\n",
       "                               ...   \n",
       "2018-02-26  JCSC_EXAMPLE          0.0\n",
       "2018-02-27  JCSC_EXAMPLE          0.0\n",
       "2018-02-28  JCSC_EXAMPLE          0.0\n",
       "2018-03-01  JCSC_EXAMPLE          0.0\n",
       "2018-03-02  JCSC_EXAMPLE          0.0\n",
       "2018-03-05  JCSC_EXAMPLE          0.0\n",
       "2018-03-06  JCSC_EXAMPLE          0.0\n",
       "2018-03-07  JCSC_EXAMPLE          0.0\n",
       "2018-03-08  JCSC_EXAMPLE      12110.0\n",
       "2018-03-09  JCSC_EXAMPLE      12090.0\n",
       "2018-03-12  JCSC_EXAMPLE      12030.0\n",
       "2018-03-13  JCSC_EXAMPLE      12020.0\n",
       "2018-03-14  JCSC_EXAMPLE      11920.0\n",
       "2018-03-15  JCSC_EXAMPLE      11710.0\n",
       "2018-03-16  JCSC_EXAMPLE      11640.0\n",
       "2018-03-19  JCSC_EXAMPLE      11830.0\n",
       "2018-03-20  JCSC_EXAMPLE      11820.0\n",
       "2018-03-21  JCSC_EXAMPLE      11900.0\n",
       "2018-03-22  JCSC_EXAMPLE      11660.0\n",
       "2018-03-23  JCSC_EXAMPLE      11340.0\n",
       "2018-03-26  JCSC_EXAMPLE          0.0\n",
       "2018-03-27  JCSC_EXAMPLE          0.0\n",
       "2018-03-28  JCSC_EXAMPLE          0.0\n",
       "2018-03-29  JCSC_EXAMPLE          0.0\n",
       "2018-03-30  JCSC_EXAMPLE          0.0\n",
       "2018-04-02  JCSC_EXAMPLE          0.0\n",
       "2018-04-03  JCSC_EXAMPLE          0.0\n",
       "2018-04-04  JCSC_EXAMPLE          0.0\n",
       "2018-04-09  JCSC_EXAMPLE          0.0\n",
       "2018-04-10  JCSC_EXAMPLE      11420.0\n",
       "Length: 64, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.market_value.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>amount</th>\n",
       "      <th>cash</th>\n",
       "      <th>order_id</th>\n",
       "      <th>realorder_id</th>\n",
       "      <th>trade_id</th>\n",
       "      <th>account_cookie</th>\n",
       "      <th>commission</th>\n",
       "      <th>tax</th>\n",
       "      <th>message</th>\n",
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       "      <td>186295.00</td>\n",
       "      <td>Order_FemEzpZ0</td>\n",
       "      <td>Order_FemEzpZ0</td>\n",
       "      <td>Trade_TQzDbd0g</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>2018-01-03 00:00:00</td>\n",
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       "      <td>-1000</td>\n",
       "      <td>199806.47</td>\n",
       "      <td>Order_mnKfLY0M</td>\n",
       "      <td>Order_mnKfLY0M</td>\n",
       "      <td>Trade_ldhSrMYa</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>13.53</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-12 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>13.55</td>\n",
       "      <td>1000</td>\n",
       "      <td>186251.47</td>\n",
       "      <td>Order_nWrOyhV4</td>\n",
       "      <td>Order_nWrOyhV4</td>\n",
       "      <td>Trade_AkSzOM7i</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-29 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>13.93</td>\n",
       "      <td>-1000</td>\n",
       "      <td>200162.54</td>\n",
       "      <td>Order_ygQNXbEJ</td>\n",
       "      <td>Order_ygQNXbEJ</td>\n",
       "      <td>Trade_HW4C7STK</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>13.93</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-08 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>12.11</td>\n",
       "      <td>1000</td>\n",
       "      <td>188047.54</td>\n",
       "      <td>Order_osmdlg2w</td>\n",
       "      <td>Order_osmdlg2w</td>\n",
       "      <td>Trade_UpRMLDi9</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-03-26 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>11.03</td>\n",
       "      <td>-1000</td>\n",
       "      <td>199061.51</td>\n",
       "      <td>Order_07LT6nZE</td>\n",
       "      <td>Order_07LT6nZE</td>\n",
       "      <td>Trade_kQDMnIJH</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>11.03</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-04-10 00:00:00</td>\n",
       "      <td>000001</td>\n",
       "      <td>11.42</td>\n",
       "      <td>1000</td>\n",
       "      <td>187636.51</td>\n",
       "      <td>Order_r9Mdl61s</td>\n",
       "      <td>Order_r9Mdl61s</td>\n",
       "      <td>Trade_zA7EdDj0</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime    code  price  amount       cash        order_id  \\\n",
       "0  2018-01-02 00:00:00  000001  13.70    1000  186295.00  Order_FemEzpZ0   \n",
       "1  2018-01-03 00:00:00  000001  13.53   -1000  199806.47  Order_mnKfLY0M   \n",
       "2  2018-01-12 00:00:00  000001  13.55    1000  186251.47  Order_nWrOyhV4   \n",
       "3  2018-01-29 00:00:00  000001  13.93   -1000  200162.54  Order_ygQNXbEJ   \n",
       "4  2018-03-08 00:00:00  000001  12.11    1000  188047.54  Order_osmdlg2w   \n",
       "5  2018-03-26 00:00:00  000001  11.03   -1000  199061.51  Order_07LT6nZE   \n",
       "6  2018-04-10 00:00:00  000001  11.42    1000  187636.51  Order_r9Mdl61s   \n",
       "\n",
       "     realorder_id        trade_id account_cookie  commission    tax message  \n",
       "0  Order_FemEzpZ0  Trade_TQzDbd0g   JCSC_EXAMPLE           5   0.00    None  \n",
       "1  Order_mnKfLY0M  Trade_ldhSrMYa   JCSC_EXAMPLE           5  13.53    None  \n",
       "2  Order_nWrOyhV4  Trade_AkSzOM7i   JCSC_EXAMPLE           5   0.00    None  \n",
       "3  Order_ygQNXbEJ  Trade_HW4C7STK   JCSC_EXAMPLE           5  13.93    None  \n",
       "4  Order_osmdlg2w  Trade_UpRMLDi9   JCSC_EXAMPLE           5   0.00    None  \n",
       "5  Order_07LT6nZE  Trade_kQDMnIJH   JCSC_EXAMPLE           5  11.03    None  \n",
       "6  Order_r9Mdl61s  Trade_zA7EdDj0   JCSC_EXAMPLE           5   0.00    None  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.history_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>account_cookie</th>\n",
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       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th>account_cookie</th>\n",
       "      <th></th>\n",
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       "      <th>2018-01-02 00:00:00</th>\n",
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       "      <th>JCSC_EXAMPLE</th>\n",
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       "      <th>2018-01-29 00:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
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       "      <td>2018-01-29 00:00:00</td>\n",
       "      <td>2018-01-29</td>\n",
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       "      <th>2018-03-08 00:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>188048</td>\n",
       "      <td>2018-03-08 00:00:00</td>\n",
       "      <td>2018-03-08</td>\n",
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       "      <th>2018-03-26 00:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>199062</td>\n",
       "      <td>2018-03-26 00:00:00</td>\n",
       "      <td>2018-03-26</td>\n",
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       "      <th>2018-04-10 00:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>187637</td>\n",
       "      <td>2018-04-10 00:00:00</td>\n",
       "      <td>2018-04-10</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      cash             datetime       date  \\\n",
       "datetime            account_cookie                                           \n",
       "2018-01-02 00:00:00 JCSC_EXAMPLE    186295  2018-01-02 00:00:00 2018-01-02   \n",
       "2018-01-03 00:00:00 JCSC_EXAMPLE    199806  2018-01-03 00:00:00 2018-01-03   \n",
       "2018-01-12 00:00:00 JCSC_EXAMPLE    186251  2018-01-12 00:00:00 2018-01-12   \n",
       "2018-01-29 00:00:00 JCSC_EXAMPLE    200163  2018-01-29 00:00:00 2018-01-29   \n",
       "2018-03-08 00:00:00 JCSC_EXAMPLE    188048  2018-03-08 00:00:00 2018-03-08   \n",
       "2018-03-26 00:00:00 JCSC_EXAMPLE    199062  2018-03-26 00:00:00 2018-03-26   \n",
       "2018-04-10 00:00:00 JCSC_EXAMPLE    187637  2018-04-10 00:00:00 2018-04-10   \n",
       "\n",
       "                                   account_cookie  \n",
       "datetime            account_cookie                 \n",
       "2018-01-02 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-03 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-12 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-29 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-03-08 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-03-26 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-10 00:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.cash_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2018-01-02    199995.00\n",
       "2018-01-03    199806.47\n",
       "2018-01-04    199806.47\n",
       "2018-01-05    199806.47\n",
       "2018-01-08    199806.47\n",
       "2018-01-09    199806.47\n",
       "2018-01-10    199806.47\n",
       "2018-01-11    199806.47\n",
       "2018-01-12    199801.47\n",
       "2018-01-15    200451.47\n",
       "2018-01-16    200451.47\n",
       "2018-01-17    200481.47\n",
       "2018-01-18    200971.47\n",
       "2018-01-19    201051.47\n",
       "2018-01-22    200691.47\n",
       "2018-01-23    200901.47\n",
       "2018-01-24    200891.47\n",
       "2018-01-25    200451.47\n",
       "2018-01-26    200301.47\n",
       "2018-01-29    200162.54\n",
       "2018-01-30    200162.54\n",
       "2018-01-31    200162.54\n",
       "2018-02-01    200162.54\n",
       "2018-02-02    200162.54\n",
       "2018-02-05    200162.54\n",
       "2018-02-06    200162.54\n",
       "2018-02-07    200162.54\n",
       "2018-02-08    200162.54\n",
       "2018-02-09    200162.54\n",
       "2018-02-12    200162.54\n",
       "                ...    \n",
       "2018-02-26    200162.54\n",
       "2018-02-27    200162.54\n",
       "2018-02-28    200162.54\n",
       "2018-03-01    200162.54\n",
       "2018-03-02    200162.54\n",
       "2018-03-05    200162.54\n",
       "2018-03-06    200162.54\n",
       "2018-03-07    200162.54\n",
       "2018-03-08    200157.54\n",
       "2018-03-09    200137.54\n",
       "2018-03-12    200077.54\n",
       "2018-03-13    200067.54\n",
       "2018-03-14    199967.54\n",
       "2018-03-15    199757.54\n",
       "2018-03-16    199687.54\n",
       "2018-03-19    199877.54\n",
       "2018-03-20    199867.54\n",
       "2018-03-21    199947.54\n",
       "2018-03-22    199707.54\n",
       "2018-03-23    199387.54\n",
       "2018-03-26    199061.51\n",
       "2018-03-27    199061.51\n",
       "2018-03-28    199061.51\n",
       "2018-03-29    199061.51\n",
       "2018-03-30    199061.51\n",
       "2018-04-02    199061.51\n",
       "2018-04-03    199061.51\n",
       "2018-04-04    199061.51\n",
       "2018-04-09    199061.51\n",
       "2018-04-10    199056.51\n",
       "Name: 0, Length: 64, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb25356160>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.assets.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb257f74a8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.benchmark_assets.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.plot_assets_curve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.plot_dailyhold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.plot_signal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total_buyandsell': -870.0,\n",
       " 'total_tax': -38.49,\n",
       " 'total_commission': -35.0,\n",
       " 'total_profit': -943.49}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.profit_construct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Performance=QA.QA_Performance(Account)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sell_date</th>\n",
       "      <th>buy_date</th>\n",
       "      <th>amount</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>buy_price</th>\n",
       "      <th>pnl_ratio</th>\n",
       "      <th>pnl_money</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000001</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>1000</td>\n",
       "      <td>13.53</td>\n",
       "      <td>13.70</td>\n",
       "      <td>-0.012409</td>\n",
       "      <td>-170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000001</th>\n",
       "      <td>2018-01-29</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>1000</td>\n",
       "      <td>13.93</td>\n",
       "      <td>13.55</td>\n",
       "      <td>0.028044</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000001</th>\n",
       "      <td>2018-03-26</td>\n",
       "      <td>2018-03-08</td>\n",
       "      <td>1000</td>\n",
       "      <td>11.03</td>\n",
       "      <td>12.11</td>\n",
       "      <td>-0.089182</td>\n",
       "      <td>-1080.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sell_date   buy_date  amount  sell_price  buy_price  pnl_ratio  \\\n",
       "code                                                                     \n",
       "000001 2018-01-03 2018-01-02    1000       13.53      13.70  -0.012409   \n",
       "000001 2018-01-29 2018-01-12    1000       13.93      13.55   0.028044   \n",
       "000001 2018-03-26 2018-03-08    1000       11.03      12.11  -0.089182   \n",
       "\n",
       "        pnl_money  \n",
       "code               \n",
       "000001     -170.0  \n",
       "000001      380.0  \n",
       "000001    -1080.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Performance.pnl_fifo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Performance.plot_pnlmoney(Performance.pnl_fifo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP6: 存储结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Account.save()\n",
    "Risk.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP7: 查看存储的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "account_info=QA.QA_fetch_account({'account_cookie':'JCSC_EXAMPLE'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "account=QA.QA_Account().from_message(account_info[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "< QA_Account JCSC_EXAMPLE>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
